BDPS: An efficient spark-based big data processing scheme for cloud Fog-IoT orchestration
Date
Authors
ORCID
https://orcid.org/0000-0003-1289-6484
https://orcid.org/0000-0001-9306-9637
https://orcid.org/0000-0001-8980-6841
https://orcid.org/0000-0001-5822-3432
https://orcid.org/0000-0002-1763-2544
https://orcid.org/0000-0003-2822-0657
https://orcid.org/0000-0003-1289-6484
https://orcid.org/0000-0001-9306-9637
https://orcid.org/0000-0001-8980-6841
https://orcid.org/0000-0001-5822-3432
https://orcid.org/0000-0002-1763-2544
Journal Title
Journal ISSN
Volume Title
Publisher
Abstract
The Internet of Things (IoT) has seen a surge in mobile devices with the market and technical expansion. IoT networks provide end-to-end connectivity while keeping minimal latency. To reduce delays, efficient data delivery schemes are required for dispersed fog-IoT network orchestrations. We use a Spark-based big data processing scheme (BDPS) to accelerate the distributed database (RDD) delay efficient technique in the fogs for a decentralized heterogeneous network architecture to reinforce suitable data allocations via IoTs. We propose BDPS based on Spark-RDD in fog-IoT overlay architecture to address the performance issues across the network orchestration. We evaluate data processing delays from fog-IoT integrated parts using a depth-first-search-based shortest path node finding configuration, which outperforms the existing shortest path algorithms in terms of algorithmic (i.e., depth-first search) efficiency, including the Bellman–Ford (BF) algorithm, Floyd–Warshall (FW) algorithm, Dijkstra algorithm (DA), and Apache Hadoop (AH) algorithm. The BDPS exhibits low latency in packet deliveries as well as low network overhead uplink activity through a map-reduced resilient data distribution mechanism, better than in BF, DA, FW, and AH. The overall BDPS scheme supports efficient data delivery across the fog-IoT orchestration, outperforming faster node execution while proving effective results, compared to DA, BF, FW and AH, respectively.